Industrial controllers are special-purpose computers utilized for controlling industrial processes, manufacturing equipment, and other factory automation, such as data collection or networked systems. One type of industrial controller at the core of an industrial control system is a logic processor such as a programmable logic controller (PLC) or personal computer (PC) based controller. Programmable logic controllers for instance, are programmed by systems designers to operate manufacturing processes via user-designed logic programs or user programs. The user programs are stored in memory and generally executed by the PLC in a sequential manner although instruction jumping, looping and interrupt routines, for example, are also common. Associated with the user program are a plurality of memory elements or variables that provide dynamics to PLC operations and programs.
Connected to the PLC are input/output (I/O) devices. I/O devices provide connection to the PLC for both automated data collection devices such as limit switches, photoeyes, load cells, thermocouples, etc. and manual data collection devices such as keypads, keyboards, pushbuttons, etc. Differences in PLCs are typically dependent on number of I/O they can process, amount of memory, number and type instructions and speed of the PLC central processing unit (CPU).
Another type of industrial controller at the core of an industrial control system is the process controller of a distributed control system (DCS). The process controller is typically programmed by a control engineer for continuous process control such as an oil refinery or a bulk chemical manufacturing plant. A control engineer typically configures control elements such as proportional-integral-derivative (PID) control loops to continuously sample the I/O data, known as the process variable, from the process, compare the process variable to a configured set point and output an error signal, proportional to the difference between the set point and the process variable, to the control device. The control device then adjusts the element controlling the process property, such as a valve in a pipe for flow control or a heating element in a distillation column for temperature control, in an attempt to minimize the error signal. As the DCS name implies, many process controllers are distributed around the process and are communicatively coupled to each other forming the overall control system.
Connected to the process controller are similar types of I/O devices as connected to the PLC and additionally, intelligent I/O devices more common to the process control industry. These intelligent devices have embedded processors capable of performing further calculations or linearization of the I/O data before transmission to the process controller.
A visualization system is generally connected to the industrial controller to provide a human-friendly view into the process instrumented for monitoring or control. The user of a visualization system configures one or more graphical displays representing some aspect of the process the industrial controller is controlling or monitoring. The graphical displays each contain a user configured number of data values collected from the I/O connected to the industrial controller and considered by the user as relevant to the particular graphical display or process area of interest. Other data points may be configured strictly for archival purposes or to generate reports related to interests such as production, downtime, operator efficiency, raw material usage, etc.
Although the visualization system effectively represents the process of interest and provides a means for the operator to monitor or control the process, the intelligence to troubleshoot the process, recognize patterns that will most probably lead to downtime or determine the most expedient action to take to return the process to optimal operating conditions remains knowledge held by the operator. Operators develop an intimate understanding of the process and its unit operations over long periods of time spent managing operations. During this time, the operator, through experience, develops a feel for whether the process is operating at peak efficiency based on familiarity with the process. In some cases a visualization system can collect relevant data but in many cases, without the operators' guidance, it is difficult or impossible to associate a particular set of process conditions with a particular process problem.
However, the presence of an experienced operator does not completely address the scope of problems that can be associated with a manufacturing process. Many industrial process problems are not apparent until they are viewed in light of process conditions outside of the operators' view. For instance, product quality analysis based on laboratory techniques or sophisticated chemical analysis. Other problems such as raw material availability may not be visible to the operator. Addressing these higher level and more complex problems requires the use of Enterprise Manufacturing Intelligence (EMI) systems and access by these systems to process data. After the EMI system has analyzed the data in combination with other data such as material availability and business requirements, the EMI system generates daily, weekly or monthly workflow schedules and maintenance work orders based on the best available data.
The power of this information as it is fed back to the localized process operators and engineers has created market pressure to automate the cycle of providing process data to an EMI system and receiving and acting on the results of the analysis performed by the EMI system. As the cycle time is reduced, the requirements for warehousing raw materials and lead time for fulfilling product orders can be reduced resulting in a more efficient and profitable manufacturing process.